Acquiring 3D (three-dimensional) information is important for different types of applications such as autonomous vehicles, mapping aerial imaging, user interaction with computers (e.g. computer games), the movie industry (e.g., removing background), etc. In order for a vehicle to navigate autonomously, similarly to a human driver, an autonomous vehicle needs to understand the 3D world around it. The vehicle needs to know what the road “looks like”; it must identify how far the next junction is, whether there are obstacles around it, whether there are pedestrians or other vehicles approaching it, etc. All this has to be done in real time while driving under various road conditions. In addition a system that acquires the 3D information for enabling automatic navigation must be able to work at a distance and speed that allows enough time for the vehicle to respond.
During the last decade the field of autonomous vehicles has gained strategic importance and widespread relevance. Many projects were launched worldwide aimed at analyzing the problem of people's mobility and goods transportation from a number of different perspectives. During the last few years, the first prototypes of vehicles equipped with automatic driving facilities and road infrastructures supporting these functionalities, have been tested and demonstrated to the public. For the vehicle to navigate autonomously the vehicle needs to behave like a driver and must understand the 3D world around him. Furthermore the vehicle needs to know what the road looks like, be able to identify how far the next junction is, see whether there are obstacles around him, recognize whether there are pedestrians or other vehicles approaching him, etc. Using 3D information enables the robot or autonomous vehicle to detect obstacles and to avoid them, to recognize the objects, to map the environment and to select its route.
Today, there are two practical technologies available for acquiring 3D images: a) stereo vision methodologies; and b) using Range Laser Sensors which employ Time-Of-Flight (TOF) techniques to measure distance. Both technologies have been used separately and in combination, but, unfortunately they have failed to provide a robust solution when applied to autonomous vehicles.
Similar to human vision, stereo vision uses at least two views of the same object to measure distance. To calculate distance, it is necessary to identify similar objects or points with high resolution in the simultaneously captured frames. Then, by measuring the distance between the cameras and their angle to the object, the distance to the object can be estimated. Common limitations of this technique are: the amount of computation needed in order to find the similar objects and to make the calculations, the errors that occur when there is no pattern or duplicated object on the surface, the limited accuracy that is the result of the distance between the cameras and the pixel size, and the sensitivity to light conditions and texture.
The existing range sensors technology that supports autonomous vehicles today employs TOF detector to measure distance. The TOF methodology usually operates as follows: A light pulse is emitted by a laser diode. The light travels until it hits a target, and a portion of the light's energy is reflected back towards the emitter. A detector located near the emitter detects the returning signal and calculates the time difference between the time the laser emitted the pulse and the time when the pulse was detected. This time difference determines the distance of the target. In order to capture a 3D image, the laser pulse is directed each time to a different angle. Based on the directions the laser was directed to, an array or a matrix of distance measurements is generated. The assembly of these measurements composes a 3D distance image. Although this technology is widely used for research and prototyping in autonomous vehicles, it suffers both from high price and from low reliability due to components that move during the scanning. It is also susceptible to crosstalk and noise.
To address these problems and to provide real-time 3D information for autonomous vehicle navigation, enhanced imaging tools are required.
It is an object of the present invention to provide a system which is capable of combining 3D low resolution image capturing technique and stereoscopy technique as complementary methods and provide higher resolution 3D images
It is an object of the present invention to provide a safe and reliable solution for autonomous vehicles.
Other objects and advantages of the invention will become apparent as the description proceeds.